The binding of Cu, Fe, Mn, and Zn to proteins in blood and in blood fractions was investigated, since their interactions in free radical metabolism in humans is of great interest. An HPLC-ICP-AES technique was developed allowing adequate separation of metalloproteins and of inorganic and organic metal species. For the separation of metalloproteins in erythrocytes and blood plasma a Merck Superformance Fractogel EMD BioSEC 650 (S) column was used. Size exclusion chromatography (SEC)-HPLC was hyphenated to ICP-AES both on-line and off-line for the detection of trace elements in the fractions resulting from HPLC separations. HPLC parameters, pH, temperature, flow rate and salt concentration were optimized for the protein separation and the optimal conditions were applied for the hyphenation to the ICP-AES detector. The separation column was calibrated with five standard proteins. For the element determination by ICP-AES a line selection with respect to the sensitivity was performed. Three different methods were used for the determination of trace elements in blood: direct determinations, on-line and off-line SEC-HPLC-ICP-AES measurements. For the optimizing experiments blood samples of one female subject were used. The direct determination by ICP-AES of the elements was performed in blood and blood fractions of ten different subjects to obtain the average concentration ranges. From the results the identification of the protein Cu/Zn superoxide dismutase in erythrocytes was possible. The LOD were 0.03 microgram mL-1 for Cu, 0.026 microgram mL-1 for Fe, 0.8 ng mL-1 for Mn, and 0.09 microgram mL-1 for Zn in a synthetic blood matrix.
The urinary stone, serum and 24-hour urine concentrations of 14 trace elements were determined in urinary stone patients by inductively coupled plasma atomic-emission spectroscopy. The data obtained for 25 active stone patients and 32 whose last stone episode had occurred at least 12 months previously were compared with those of 25 healthy individuals. Urinary nickel, manganese and lithium excretion, and serum nickel, manganese and cadmium concentrations were statistically significantly lower for active stone patients compared to those with previous stone episodes and healthy individuals. No difference in the concentrations of trace elements could be found, however, for patients with previous stone episodes and healthy individuals. Nickel, manganese, lithium and cadmium could be of significance in the pathological mechanism of stone formation, not from mineralogical or crystallographic viewpoints but for the smooth flow of enzymatic reactions in biological systems.
This paper develops a method to efficiently estimate hidden Markov models with continuous latent variables using maximum likelihood estimation. To evaluate the (marginal) likelihood function, I decompose the integral over the unobserved state variables into a series of lower dimensional integrals, and recursively approximate them using numerical quadrature and interpolation. I show that this procedure has very favorable numerical properties: First, the computational complexity grows linearly in the number of periods, making the integration over hundreds and thousands of periods feasible. Second, I prove that the numerical error accumulates sublinearly in the number of time periods integrated, so the total error can be well controlled for a very large number of periods using, for example, Gaussian quadrature and Chebyshev polynomials. I apply this method to the bus engine replacement model of Rust [Econometrica 55(5): 999-1033] to verify the accuracy and speed of the procedure in both actual and simulated data sets.
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